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17 September 2018 X-ray dual-energy computed tomography by use of a filter-strip array for medical applications (Conference Presentation)
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Abstract
A single-scan dual-energy low-dose cone-beamCT (CBCT) imaging technique that exploits a filter-strip array is summarized in this paper. The filter-strip array installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, the algorithm exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The feasibility of the proposed technique has been earlier demonstrated by the use of various phantoms in the experimental CBCT setup. Based on the proposed dual-energy imaging, a material differentiation was also performed and its potential utility has been shown. In this work, we summarize the technique emphasizing task-specific optimization nature of the imaging in medical applications. A choice of beam-filtering material and its thickness, filter-strip array design, scanning configurations, and image reconstruction algorithm have been systematically investigated therefore.
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© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Seungryong Cho, Donghyeon Lee, and Taejin Kwon "X-ray dual-energy computed tomography by use of a filter-strip array for medical applications (Conference Presentation)", Proc. SPIE 10727, UV and Higher Energy Photonics: From Materials to Applications 2018, 107270H (17 September 2018); https://doi.org/10.1117/12.2324243
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